Variational Data Assimilation & Model Learning- I5MMAD11

MODULE TYPE: SDG11
MODULE DESCRIPTION
Inverse modelling; fusing datasets with physical models; calibrating PDE models; Learning model terms from large datasets
LEARNING OUTCOMES
At the end, the students are supposed to be able : - To set up a data assimilation inverse formulation e.g. in (geophysical) fluid mechanics, structural mechanics, biology etc in presence of databases, measurements. - To compute efficiently the gradient with a large number of parameters by deriving the adjoint model. - To design the complete optimisation process, - To perform local sensitivities analysis, to calibrate the model, to estimate uncertain parameters by assimilating the data into the physical-based model (PDE but also potentially SDE). - To learn model terms (ODE or PDE) from datasets.
Module level: Advanced
Degree level: Masters
Language: English
Study mode: Online Possible
SDG11 Theme:
ECTS: 3
Semester: Autumn
Starting date: October, 2021
Finishing date: January, 2022
Enrollment period:
Participation form: Please fill this document and send it to the admission-contact person: Marie Agnès Detourbe, detourbe@insa-toulouse.fr